Parametric optimal estimation retrieval of the non-precipitating parameters over the global oceans, A

2006 Summer.Includes bibliographical references (pages 82-87).Covers not scanned.Print version deaccessioned 2021.There are a multitude of spacebome microwave sensors in orbit, including the TRMM Microwave Imager (TMI), the Special Sensor Microwave/lmager (SSM/I) onboard the DMSP satellites, the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), SSMIS, WINDSAT, and others. Future missions, such as the planned Global Precipitation Measurement (GPM) Mission, will incorporate additional spacebome microwave sensors. The need for consistent geophysical parameter retrievals among an ever-increasing number of microwave sensors requires the development of a physical retrieval scheme independent of any particular sensor and flexible enough so that future microwave sensors can be added with relative ease. To this end, we attempt to develop a parametric retrieval algorithm currently applicable to the non-precipitating atmosphere with the goal of having consistent non-precipitating geophysical parameter products. An algorithm of this nature makes is easier to merge separate products, which, when combined, would allow for additional global sampling or longer time series of the retrieved global geophysical parameters for climate purposes. This algorithm is currently applied to TMI, SSM/I and AMSR-E with results that are comparable to other independent microwave retrievals of the non-precipitating parameters designed for specific sensors. The physical retrieval is developed within the optimal estimation framework. The development of the retrieval within this framework ensures that the simulated radiances corresponding to the retrieved geophysical parameters will always agree with observed radiances regardless of the sensor being used. Furthermore, a framework of this nature allows one to easily add additional physics to describe radiation propagation through raining scenes, thus allowing for the merger of cloud and precipitation retrievals, if so desired. Additionally, optimal estimation provides error estimates on the retrieval, a product often not available in other algorithms, information on potential forward model/sensor biases, and a number of useful diagnostics providing information on the validity and significance of the retrieval (such as Chi-Square, indicative of the general "fit" between the model and observations and the A-Matrix, indicating the sensitivity of the model to a change in the geophysical parameters). There is an expected global response of these diagnostics based on the scene being observed, such as in the case of a raining scene. Fortunately, since TRMM has a precipitation radar (TRMM PR) in addition to a radiometer (TMI) flying on-board, the expected response of the retrieval diagnostics to rainfall can be evaluated. It is shown that a potentially powerful rainfall screen can then be developed for use in passive microwave rainfall and cloud property retrieval algorithms with the possibility of discriminating between precipitating and nonprecipitating scenes, and further indicating the possible contamination of rainfall in cloud liquid water path microwave retrievals

High-Resolution Analysis of Snow Albedo Interactions in the Arctic

The Snow Albedo Feedback (SAF) is an important contributor to Arctic warming, however models disagree significantly on the strength of this effect. Previous work has investigated the influence of vegetation on surface albedo, however the accuracy has been limited by the resolution of model output. In this work, we perform a pan-Arctic survey using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and Coupled Model Intercomparison Project (CMIP6) model output, to perform a comprehensive analysis of the effects of vegetation on SAF. We computed pan-Arctic composites of MODIS observational data at the 500m scale and compared the results with the CMIP6 ensemble. Using MODIS data, we found a mean SAF of -2.17 % ⋅ K−1 from April-July over the climatological period 2001-2019, which is stronger than predicted by the CMIP6 intermodel mean. Additionally, we identified the source of this discrepancy - models currently do not adequately capture the dynamics of late-season melt-off in the high Arctic grassland and barren regions, which results in an underestimate of SAF. In this work, we demonstrate that land cover changes have a small but nonzero (≤ 10%) contribution to overall changes in SAF on the timescale of decades, indicating the importance of dynamic vegetation models. Furthermore, we identify upscaling resolution as a major source of local error in SAF, however due to cancellation of errors this has minimal impact on estimates of pan- Arctic mean SAF. Finally, we identify a logarithmic relationship between LAI (Leaf Area Index) and SAF. This work can benefit modelling groups seeking to better capture SAF dynamics, by explaining SAF errors in terms of vegetation dynamics, and by demonstrating the existence of spatial structure in SAF fields at sub-model grid scales

Enhancing Tropospheric Humidity Data Records from Satellite Microwave and Radiosonde Sensors

Water vapor is the most dominant greenhouse gas and plays a critical role in the climate by regulating the Earth's radiation budget and hydrological cycle. A comprehensive dataset is required to describe the temporal and spatial distribution of water vapor, evaluate the performance of climate and weather prediction models in terms of simulating tropospheric humidity, and understand the role of water vapor and its feedback in the climate system. Satellite microwave and radiosonde measurements are two important sources of tropospheric humidity. However, both datasets are subject to errors and uncertainties. The goal of this dissertation was to develop techniques for quantifying and correcting errors in both radiosonde and microwave satellite data. These techniques can be used to homogenize the datasets in order to develop tropospheric humidity climate data records. The quality of operational radiosonde data were investigated for different sensor types. It was found that the use of a variety of sensors over the globe introduces temporal and spatial errors in the data. Further, it was shown that the daytime radiation dry bias, which is one the most important errors in radiosonde data, depends on both sensor type and radiosonde launch time. The error significantly decreases if daytime data are collected near sunrise or sunset. Radiometric errors in satellite data were investigated using both intercomparison of coincident observations as well as validation versus high-quality radiosonde and global positioning system radio occultation data. The results showed that the data from recently launched microwave sounders have a good accuracy relative to each other and simulated data. However, the absolute accuracy of the microwave satellite data can still not be validated due to the lack of reference measurements. In addition, a novel technique for correcting geolocation errors in microwave satellite data was developed based on the difference between ascending and descending observations along the coastlines. Using this method, several important errors including timing errors up to a few hundred milliseconds, and sensor mounting errors up to 1.2 degree were found in some of the microwave instruments. Finally, since satellite data are indirect measurements, a method was developed to transform satellite radiances from different water vapor channels to layer averaged humidity. The technique is very fast because radiative transfer calculations are only required to determine the empirical coefficients

Synthesis of Satellite Microwave Observations for Monitoring Global Land-Atmosphere CO2 Exchange

This dissertation describes the estimation, error quantification, and incorporation of land surface information from microwave satellite remote sensing for modeling global ecosystem land-atmosphere net CO2 exchange. Retrieval algorithms were developed for estimating soil moisture, surface water, surface temperature, and vegetation phenology from microwave imagery timeseries. Soil moisture retrievals were merged with model-based soil moisture estimates and incorporated into a light-use efficiency model for vegetation productivity coupled to a soil decomposition model. Results, including state and uncertainty estimates, were evaluated with a global eddy covariance flux tower network and other independent global model- and remote-sensing based products

POTENTIAL CONTRASTS IN CO2 AND CH4 FLUX RESPONSE UNDER CHANGING CLIMATE CONDITIONS: A SATELLITE REMOTE SENSING DRIVEN ANALYSIS OF THE NET ECOSYSTEM CARBON BUDGET FOR ARCTIC AND BOREAL REGIONS

The impact of warming on the net ecosystem carbon budget (NECB) in Arctic-boreal regions remains highly uncertain. Heightened CH4 emissions from Arctic-boreal ecosystems could shift the northern NECB from an annual carbon sink further towards net carbon source. Northern wetland CH4 fluxes may be particularly sensitive to climate warming, increased soil temperatures and duration of the soil non-frozen period. Changes in northern high latitude surface hydrology will also impact the NECB, with surface and soil wetting resulting from thawing permafrost landscapes and shifts in precipitation patterns; summer drought conditions can potentially reduce vegetation productivity and land sink of atmospheric CO2 but also moderate the magnitude of CH4 increase. The first component of this work develops methods to assess seasonal variability and longer term trends in Arctic-boreal surface water inundation from satellite microwave observations, and quantifies estimate uncertainty. The second component of this work uses this information to improve understanding of impacts associated with changing environmental conditions on high latitude wetland CH4 emissions. The third component focuses on the development of a satellite remote sensing data informed Terrestrial Carbon Flux (TCF) model for northern wetland regions to quantify daily CH4 emissions and the NECB, in addition to vegetation productivity and landscape CO2 respiration loss. Finally, the fourth component of this work features further enhancement of the TCF model by improving representation of diverse tundra and boreal wetland ecosystem land cover types. A comprehensive database for tower eddy covariance CO2 and CH4 flux observations for the Arctic-boreal region was developed to support these efforts, providing an assessment of the TCF model ability to accurately quantify contemporary changes in regional terrestrial carbon sink/source strength

Assimilation des observations satellitaires au-dessus des surfaces continentales

Dans les modèles de prévision numérique du temps, les observations satellitaires sont devenues indispensables pour la production d'une analyse atmosphérique optimale. Or, malgré les performances et la maturité des systèmes d'assimilation actuels, ces observations demeurent fortement sous-exploitées au-dessus des surfaces continentales pour différentes raisons. L'objectif de cette étude est d'améliorer la représentation de la surface (en température et émissivité) afin de mieux assimiler les observations de télédétection dans les modèles. Dans un premier temps, nous avons cherché à vérifier la validité des hypothèses de surface pour le calcul de l'émissivité micro-onde au dessus d'une surface enneigée (région de l'Antarctique). L'effet de plusieurs hypothèses de surface sur les émissivités micro-ondes a été étudié et la qualité des simulations de températures de brillance a été sensiblement améliorée par la prise en compte d'une hypothèse pertinente. Par la suite l'objectif était d'étendre l'assimilation des données infrarouges sensibles aux surfaces continentales qui étaient jusque là rejetées des systèmes d'assimilation. Les recherches récemment effectuées pour l'assimilation des données micro-ondes au-dessus des continents, ont montré qu'un tel objectif est atteignable si la surface est mieux caractérisée. J'ai consacré une bonne partie de ma thèse a évaluer le potentiel d'une estimation de l'émissivité et de la température de surface à partir des données du radiomètre SEVIRI (Spinning Enhanced Visible and Infrared Imager) embarqué sur MSG (METEOSAT SECONDE GENERATION). La forte sensibilité aux nuages et les biais assez marqués de la température de surface analysée dans ALADIN m'ont poussée à préférer l'utilisation de climatologies d'émissivités IR du Land-SAF (EUMET-SAT Land Surface Analysis - Satellite Application Facilities) plutôt que d'estimer directement ces valeurs à partir des observations. J'ai montré qu'en me basant sur cette climatologie, on pouvait restituer des températures de surface à partir du canal IR10.8 de même qualité que celles du Land-SAF et que l'utilisation de cette température de surface comme condition aux limites au modèle de transfert radiatif permet d'obtenir de bien meilleures simulations aux canaux SEVIRI. Enfin, des expériences d'assimilation, au sein de deux modèles à aire limitée, ont été conduites afin d'apprécier, pour la première fois, l'impact de l'assimilation des observations IR sensibles à la surface sur la qualité des analyses et des prévisions. L'impact prépondérant fut observé sur les analyses d'humidité avec une tendance à assécher l'atmosphère en période estivale et à l'humidifier en période hivernale. Ce changement d'humidité a été évalué avec succès près de la surface à l'aide de données GPS indépendantes. L'impact sur les prévisions et sur celles des précipitations en particulier, a été jugé positif principalement sur le sud de l'Europe.In numerical weather prediction models, satellite observations are essential to perform optimal atmospheric analyses. Despite the performance and maturity of current assimilation systems, for different reasons these observations remain highly underutilized over land surfaces. This study aims to improve the description of the surface (temperature and emissivity) to better assimilate remote sensing observations in models. Initially, the validity of surface approximations used to calculate the microwave emissivity over snow surface was evaluated (over the Antarctica region). The impact of several surface approximations for microwave emissivity computation was studied and it was found that the quality of brightness temperature simulations was improved using relevant approximations. Thereafter, the objective was to extend the assimilation of infrared surface-sensitive observations over land which were until now rejected by the assimilation system. Recent researches to assimilate microwave observations over land have shown that this objective can be reached with an adequatly described surface. A large part of my PhD was devoted to the evaluate the potential to retrieve land surface emissivity and land surface temperature from data provided by the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager) onboard METEO-SAT SECOND GENERATION. The strong sensitivity to clouds and the large bias found in the land surface temperature computed by the ALADIN meso-scale model encouraged me to use infrared emissivity climatology from the Land-SAF (EUMETSAT Land Surface Analysis - Satellite Application Facilities) rather than direct retrieval from SEVIRI observations. I have shown that, with these climatologies, the land surface temperature could be retrieved at channel IR10.8 with the same quality as the one from the Land-SAF. The use of this temperature as boundary conditions of the radiative transfer model improve the brightness temperature simulations at SEVIRI channels. For the first time, assimilation experiments were conduced within the two limited area models to assess the impact of the assimilation of surface-sensitive infrared observations over the analysis and forecast skills. The predominant impact was observed on the analysis of the moisture with a tendency to dry out the atmosphere in summer and increase moisture in winter. The change in moisture was successfully evaluated near the surface, using independent GPS data. The impact on forecasts, in particular the cumulative precipitation forecasts, was considered to be positive mainly over southern Europe

Suivi des évènements extrêmes de pluie sur neige dans l’Arctique Canadien à l’aide de données micro-ondes passives multi sources

Les impacts du changement climatique dans les zones nordiques sont significatifs, les évènements extrêmes se voient être multipliés par dix depuis le début des années 1980 dans les zones de toundra. L’augmentation des vagues de chaleur, mais aussi l’apparition des évènements de pluie sur neige sont de plus en plus intenses et longs durant la période hivernale, ayant ainsi un impact considérable sur les différents écosystèmes notamment en Arctique. Il est donc important de s’intéresser aux occurrences de ces évènements sporadiques en expansion dans le but de protéger et de quantifier l’impact à long terme du changement climatique sur les milieux nordiques. En effet, les évènements de pluie sur neige provoquent, après un regel rapide, des croûtes de glace ayant des conséquences sur le sol, le régime hydrique et sur l’écologie animale du milieu touché (nourriture non accessible sous la couche de glace). Diverses études ont tenté de détecter les évènements de pluies sur neige à partir de la télédétection, notamment en utilisant des données microondes passives, permettant la discrimination des différents stades métamorphiques de la neige suite aux épisodes pluvieux. Ces études bien que concluantes restent assez limitées par manque de données in-situ permettant une bonne validation de ce phénomène climatique. Les résultats présentés dans cette thèse montrent le développement d’un algorithme de détection des évènements de pluie sur neige à partir de données micro-ondes passives. Cette méthode a été validée dans un premier temps sur trois pixels AMSR-E au Nunavik avec une erreur maximale de 7%. Le second chapitre introduit une adaptation au contexte arctique de la méthode de détection des pluies sur neige, basée sur un inventaire exhaustif de 14 stations météorologiques réparties à travers l’Arctique Canadien depuis 1984. Ces résultats montrent une adaptation de la méthode de détection basée sur l’analyse de sensibilité du seuil de détection, avec une erreur d’environ 5%. La troisième partie de cette thèse porte sur l’étude des conditions de neige durant un épisode de mort massive au cours de l’hiver 2016. L’utilisation d’un jeu de données multisources (micro-ondes passives, réanalyses atmosphériques, nivales) a permis de développer une méthode de détection des couches denses. Cette étude montre que les changements de surface du manteau neigeux, provoqués par une succession de tempête au cours de l’hiver, sont la principale cause de non-accessibilité à la nourriture pour les caribous de Barren Ground de l’île du Prince Charles. Enfin, les derniers résultats présentés ici portent sur l’application de la méthode de détection des pluies sur neige sur l’ensemble de la toundra arctique canadienne ainsi que sur l’analyse spatio-temporelle de l’évolution des occurrences de pluie sur neige depuis 1979.Abstract: The impacts of climate change in the Canadian Arctic are significant, and the Arctic has experienced a significant increase in extreme events occurrence since the early 1980s. Winter extreme events such as heat waves, and rain-on-snow events are more and more intense and long during the winter period, thus having a considerable impact on the northern ecosystems. Rain-on-snow events cause changes in the energy balance through a modification of snow physical properties, thus affecting the melting regime (snow cover extent). It is therefore important to focus on event occurrence of these sporadic events in order to quantify the long-term impact of climate change in the Arctic. Of particular relevance, rain-on-snow events will lead to the formation of ice crusts that have consequences on the soil, the water regime and on foraging conditions of the various ungulates species of the arctic (i.e. caribou, muskoxen). Various studies have attempted to detect precipitation phase from remote sensing, notably by using passive microwave data, allowing the discrimination of the different metamorphic states of the snow after rain-on-snow events. These studies, although conclusive, remained rather limited because of the lack of in-situ data allowing a good validation of this phenomenon. The main results of this thesis is the development of a rain-on-snow detection algorithm using passive microwave data. This method was validated over three AMSR-E pixels in Nunavik with a maximum error of 7%. The second part of this work proposed an adaptation of the rain-on-snow detection method for a better response in Arctic conditions. This work was based on meteorological data from 14 stations in the Canadian Arctic since 1984. The sensitivity analysis in the detection threshold show a maximal error of 5%. The third part of this study investigated snow surface conditions (i.e. presence of ice crusts and/or wind slab) during a massive barren-ground caribou die-off episode in the winter 2016 on Prince Charles Island, Nunavut. This work used a multi-source dataset (passive microwave satellite measurements and modelling, atmospheric reanalysis data, in-situ snow measurements), allowing the development of a wind slab detection method from which surface density can be quantified. This study highlighted that a succession of winter storms can lead to a significant densification of the snow surface leading to very difficult grazing conditions for caribou. Finally, a pan-Arctic application of the above algorithms is presented where the rain-on-snow detection method was applied on satellite imagery across the Arctic since 1979. A spatiotemporal analysis was conducted to target specific regions where the anomalies are strongest while potential linkage with global atmospheric patterns was investigated